↓ Skip to main content

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

Overview of attention for article published in BMC Genomics, June 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

1 news outlet
12 tweeters
1 Wikipedia page


609 Dimensions

Readers on

649 Mendeley
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Published in
BMC Genomics, June 2018
DOI 10.1186/s12864-018-4772-0
Pubmed ID

Kelly Street, Davide Risso, Russell B. Fletcher, Diya Das, John Ngai, Nir Yosef, Elizabeth Purdom, Sandrine Dudoit


Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 649 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 649 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 182 28%
Researcher 117 18%
Student > Master 74 11%
Student > Bachelor 70 11%
Student > Doctoral Student 35 5%
Other 57 9%
Unknown 114 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 203 31%
Agricultural and Biological Sciences 103 16%
Immunology and Microbiology 54 8%
Medicine and Dentistry 48 7%
Computer Science 34 5%
Other 83 13%
Unknown 124 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2021.
All research outputs
of 19,529,814 outputs
Outputs from BMC Genomics
of 9,813 outputs
Outputs of similar age
of 290,713 outputs
Outputs of similar age from BMC Genomics
of 6 outputs
Altmetric has tracked 19,529,814 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,813 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 290,713 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them